Bibliographic record
Abstract
By now, any Albertans who follow the news are probably aware of something called a Power Purchase Arrangement (PPA). Up until a few months ago, the PPA holders — which included TransCanada, ENMAX, and Capital Power — were responsible for buying electricity from legacy power plants at pre-determined rates and selling it into the grid. But with power prices falling and costs rising, the PPAs are no longer profitable. So, early in 2016, they backed away from these arrangements and handed the money losing PPAs over to an entity known as the Balancing Pool. With the electricity bills of Alberta households and business in the balance, it’s been a high-stakes dispute between the companies and the government ever since. The government estimates losses to Alberta ratepayers may be up to $2 billion and alleges the regulations under which the companies terminated their PPAs are invalid. They’re going to court to try and prove it. The companies counter with substantially lower cost estimates and point to changes in government policy as a permissible reason for termination. How did we end up here? How costly will the PPA terminations really be? Given the importance of this issue, we cut through the politics and see what data has to say. There are three key pieces to the puzzle. First, in June 2015 the province strengthened rules around emissions from large industrial facilities. The new rules gave more ambitious targets to facilities and increased the charge on greenhouse gas emissions above their targets from $15 per tonne to $30. Second, in November 2015, the Climate Leadership Panel recommended leaving the price on emissions at $30 per tonne but changing the targets to treat all power producers equally regardless of their emissions intensity. For coal, this was a big hit, and the industry was recommending — and hoping for — a better deal that they didn’t get. Finally, and perhaps most importantly, electricity prices collapsed. Despite the fears of many, the new climate policies are unlikely to increase electricity prices. This makes it difficult for coal power to cover its now higher costs. Overall, we find policy changes account for roughly half the drop in PPA values, while falling prices account for the other half. The good news for Albertans is the drop in value appears to amount only to $900 million, and since one of the PPAs is already owned by Alberta’s Balancing Pool the real cost of these changes is closer to $600 million. All in, that amounts to about $2.25 a month for a typical household, but it will only show up if power prices remain very low. So, while there is a chance consumers will pay it, they will only end up doing so if they are saving far more on power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".